There has also been no work to understand the possibility of predicting edit quality based on edits in pages in a common category.

there has been no work to leverage advanced machinery developed in language modeling toward predicting edit quality.


We can see that the IR baseline tends to output responses with more overlapped terms with the query due to the use of Jaccard similarity.

the obtained responses may not be relevant to the query, as shown in the first case.


A strong thesis statement can help lay a strong foundation for the rest of the essay by organizing its content, improving its comprehensibility, and ensuring its relevance to the prompt.

an essay with a weak thesis statement lacks focus.


In this regard, while different adjectives such as heavy and strong can convey basically the same meaning (e.g., 'intensification' in heavy load and in strong fragrance), great has different senses in great loss and in great time (with 'intensification' and 'positive' meanings, respectively).

to interpret the meaning of a sentence, a system should take into account the properties of these expressions: for instance, the meaning of the verb [to] take in the collocation take [a] cab is different from the same verb in a free combination such as take [a] pencil, so natural language understanding or abstract meaning representation systems could benefit from the correct identification of collocations (Bonial et al., 2014;O'Gorman et al., 2018).


To evaluate the extraction, some researchers use manual selection of true collocations from ranked lists, while others take advantage of examples extracted from collocation dictionaries.

most of these approaches are carried out only in one language, and they do not always permit to obtain precise recall values.


On the one hand, this allows for accurate precision and recall values to be obtained, also taking into account ambiguous combinations which may be collocations or not depending on the context.

a gold-standard enables the research community to evaluate different strategies in a more comparable way.


In this respect, we could miss some true collocations incorrectly labeled with a wrong dependency relation.

the annotated cases were manually checked, and therefore they have a correct syntactic analysis (except for human errors).


On the one hand, the base of each collocation has a numerical id followed by the syntactic pattern (e.g., obj, amod) and by its lexical function.

the collocate is labeled with the same id as the base it depends on.


"Answer 0: the trophy","Answer 1: the suitcase".

wSC estimates common sense indirectly and it does not consider explanation on why one option is true while the other is wrong.


We conjure that both of them have the ability to judge whether a sentence is with or against common sense.

for Sen-Making, ELMo does better than BERT; BERT beats ELMo in Explanation.


Fine-tuned ELMo has an obvious improvement in Sen-Making and a non-obvious improvement in Explanation, probably because introducing knowledge will help models to identify common sense but cannot help them in inference.

fine-tuning makes BERT perform the same in Sen-Making and even worse in Explanation.


Language models (LMs) have been used as baselines in several humor recognition studies (Shahaf et al., 2015;Yang et al., 2015;Cattle and Ma, 2018).

to most previous humor recognition studies, we didn't engineer any linguistic features.


In contrast to most previous humor recognition studies, we didn't engineer any linguistic features.

lM-based approach can be seen as indirect reflection of some common humor features such as incongruity, unexpectedness, or nonsense.


Since the self-attention module is just a two-layer feed-forward network, the computational complexity of training CVDD is very low.

evaluating a pre-trained model for obtaining word embeddings may add to the computational cost (e.g.


NN may be the most straightforward approach.

it is often challenged by a phenomenon called hubness (Radovanovic et al., 2010).


We define a distance matrix D, between the mapped source embeddings and target embeddings, where "dist" is some distance metric.

for the ith source word, Nearest Neighbor (NN) criterion determines (the index of) its translation in the Y set, by arg min several works (Radovanovic et al., 2010;Dinu et al., 2014) have observed that the accuracy of NN is often significantly degraded by a phenomenon called hubness.


2017show that ISF significantly outperforms NN in BLI tasks.

it is still not clear why ISF works so well.


The quantity, exp(−β j / ), normalizes the j-th column of the kernel matrix exp(−D i,j / ).

(ISF) simply sets the column normalizer as the column sum, i exp(−D i,j / ).


While interacting with the human annotator, one may decide to split the total annotation budget B between two types of queries: (i) those which improve the underlying student learner based on the suggestions of the policy, and (ii) those which improve the policy.

this approach may not make the best use of the annotation budget, as it is not clear whether the budget used to improve the policy (via the second type of queries) would pay back the improvement which could have been achieved on the student learner (via the queries of the first type).


Existing approaches for learning word embeddings often assume there are sufficient occurrences for each word in the corpus, such that the representation of words can be accurately estimated from their contexts.

in real-world scenarios, out-of-vocabulary (a.k.a.


In another work (Khodak et al., 2018), a simple linear function is used to infer embedding of an OOV word by aggregating embeddings of its context words in the examples.

the simple linear averaging can fail to capture the complex semantics and relationships of an OOV word from its contexts.


However, their goal is to get a contextualized embedding based on a given sentence, with word or sub-word embeddings as input.

our work utilizes multiple contexts to learn OOV embeddings.


In our network, both the source specific part and the common part contribute positively to the source classification objective (i.e., minimize the mis-classification loss).

for the domain divergence objective, the common part contributes positively (i.e., tries to minimize divergence) whereas the source specific part contributes negatively (i.e., tries to maximize divergence).


• /σ is the correlation of two HO estimators of R in different twofold CVs, where j ̸ = j ′ and k, k ′ = 1, 2.

the six confusion matrices in M are correlated because the three partitions are performed on a single text corpus and the training sets contain overlapping samples.


The posterior distributions make an exact comparison possible (Zhang and Su, 2012;Wang and Li, 2016).

the distribution of F 1 is difficult to tackle, because it is a complex function.


The latest work is Bidirectional Beam Search (BiBS) (Sun et al., 2017) which proposes an approximate inference algorithm in BiRNN for image caption infilling.

this method is based on some unrealistic assumptions, such as that given a token, its future sequence of words is independent of its past sequence of words.


Moreover, GSN and BiBS can be only applied to decoders with bidirectional structures, while almost all sequence generative models use a unidirectional decoder.

our proposed inference method decouples from these assumptions and can be applied to the unidirectional decoder.


We can see that the complete sentences generated by them are better than all other algorithms.

biRNN-GSN uses the bidirectional structure as the decoder, which makes it challenging to apply to most sequence generative models, but the proposed method is gradient-based, which can be broadly used in any sequence generative models.


Based on the gold parse tree (Figure 1), "an extensive presence" is the maximum span of the first coreferring mention in Example 1.

the corresponding maximum boundary for this same mention is "an extensive presence, of course in this country" based on the system parse tree (Figure 2).


Compared to manually annotated minimum spans: • MINA is applicable to any English coreference corpus.

3 manually annotated minimum spans can be only used in their own corpora.


Sequence-to-sequence paradigms (Sutskever et al., 2014) provide the flexibility that the output sequence can be of a different length than the input sequence.

they still require the output vocabulary size to be fixed a priori, which limits their applicability to problems where one needs to select (or point to) an element in the input sequence; that is, the size of the output vocabulary depends on the length of the input sequence.


Remark: In our earlier attempts, we experimented with a self-attention based encoder-decoder with positional encoding similar to (Vaswani et al., 2017) to reduce the encoding time from O(n) (linear) to O(1) (constant) time.

the perfor-mance was inferior to the RNN-based encoder.


SPADE and DCRF are both sentence-level parsers.

dPLP and 2-Stage Parser are document-level parsers, and they do not report sentence-level performance.


Using event similarity alone is too coarse to support many relevant inferences.

if the relation between the events is given, more clues can be applied to support reliable inferences.


For example, a simple baseline method is randomly choosing a question asked for another paragraph, and using it as an unanswerable question.

it would be trivial to determine whether the retrieved question is answerable by using wordoverlap heuristics, because the question is irrelevant to the context (Yih et al., 2013).


In summary, the above mentioned systems aim to generate answerable questions with certain context.

our goal is to generate unanswerable questions.


", and then answer "What is the former name of that animal?".

when considering the evidence paragraphs, the question is solvable in a single hop by finding the only paragraph that describes an animal.


We report the F1 score of single-paragraph BERT on these new distractors in Table 4: the accuracy declines from 67.08 F1 to 46.84 F1.

when the same procedure is done on the training set and the model is re-trained, the accuracy increases to 60.10 F1 on the adversarial distractors.


As shown in Table 4, the original model's accuracy degrades significantly (drops to 40.73 F1).

similar to the previous setup, the model trained on the adversarially selected distractors can recover most of its original accuracy (increases to 58.42 F1).


1 Document Retrieval Knowledge bases (KBs) are considered as an essential resource for answering factoid questions.

accurately constructing KB with a welldesigned and complicated schema requires lots of human efforts, which inevitably limits the coverage of KBs (Min et al., 2013).


As a matter of fact, KBs are often incomplete and insufficient to cover full evidence required by open-domain questions.

the vast amount of unstructured text on the Internet can easily cover a wide range of evolving knowledge, which is commonly used for open-domain question answering .


Recently, text-based QA models along (Seo et al., 2016;Xiong et al., 2017;Yu et al., 2018) have achieved remarkable performance when dealing with a single passage that is guaranteed to include the answer.

they are still insufficient when multiple documents 1 https://github.com/xwhan/Knowledge-Aware-Reader.


With the entity linking annotations in passages, we fuse the entity knowledge with the token-level features in a similar fashion as the query reformulation process.

instead of applying a standard gating mechanism (Yang and Mitchell, 2017;Mihaylov and Frank, 2018), we propose a new conditional gating function that explicitly conditions on the question q .


Given the magnitude of these networks, scalably monitoring their activity necessarily relies on automated tools, and notably on NLP tools.

little is known about what characteristics texts communicated through the Darknet have, and how well off-the-shelf NLP tools do on this domain.


For some of the categories, legal and illegal activities are distinguished.

the automatic classification presented in their work focuses on the distinction between different classes of illegal activity, without addressing the distinction between legal and illegal ones, which is the subject of the present paper.


Results when training and testing on forums data are much worse for the neural-based systems, probably due to the much noisier and more varied nature of the data.

the SVM model achieves an accuracy of 85.3% in the full setting.


in Section 5), we conclude that the distribution of parts of speech alone is not as strong a signal as the word forms for distinguishing between the domains.

the SVM model does manage to distinguish between the texts even in this setting.


To reduce human efforts and scale the process, automated CTA transcript parsing is desirable.

this task has unique challenges as (1) it requires the understanding of long-range context information in conversational text; and (2) the amount of labeled data is limited and indirect-i.e., context-aware, noisy, and low-resource.


(2018) explored modeling cognitive knowledge in well-defined tasks with neural models.

for the most general setting that extract cognitive processes from interviews, we still need substantial expertise to interpret the interview transcript.


Recent advances in machine reading comprehension, textual entailment (Devlin et al., 2018) and relation extraction (Zhang et al., 2017) shows the contemporary NLP models have the capability of capturing causal relations in some degree.

it is still an open problem to extract procedural information from text.


As an application-oriented task, it is beneficial to properly introduce human efforts to monitor and optimize our expansion model.

the design of this human-model interaction faces several challenges.


Classic methods prior to the advance of applied machine learning in this domain typically try to produce grammatical English with generative grammar (Chapman and Davida, 1997).

such generation methods fall short in terms of statistical imperceptibility (Meng et al., 2008).


The last term is the entropy of the partitions at the current step which is bounded between zero and k. Hence, the KL divergence is at most k at each step.

if the probability mass is roughly evenly distributed over each of the 2 k bins, then the KL divergence is close to zero.


We observe that the GCNN outperforms the baseline models (CNN-RE/RNN-RE) in both datasets.

in the CDR dataset, the performance of GCNN is 1.6 percentage points lower than the best performing system of (Gu et al., 2017).


In their experiments, the importance of few-shot learning is not taken into account since the criminal charges that appear fewer than 80 times are filtered out.

in reality, a court is able to judge even under rare conditions.


Among those, the adversarial target input (z = z) shows the greatest decrease of 1.87 BLEU points, and removing language models have the least impact on the BLEU score.

language models are still important in reducing the size of the candidate set, regularizing word embeddings and generating fluent sentences.


As the model samples from ground truth word and the sentence-level oracle word at each step, the two sequences should have the same number of words.

we can not assure this with the naive beam search decoding algorithm.


While most neural machine translation (NMT) systems are still trained using maximum likelihood estimation, recent work has demonstrated that optimizing systems to directly improve evaluation metrics such as BLEU can substantially improve final translation accuracy.

training with BLEU has some limitations: it doesn't assign partial credit, it has a limited range of output values, and it can penalize semantically correct hypotheses if they differ lexically from the reference.


This is not surprising given that BLEU is the standard metric for system comparison at test time.

bLEU is not without problems when used as a training criterion.


From thexe examples we can see that when BLEU scores are very different, the semantics of the sentence can still be preserved.

we observe that often in these cases, the SIM scores of the sentences tend to be similar.


These documents form part of a large corpora of knowledge (e.g., research papers and encyclopedias) mostly accessible through text-based search engines.

to better exploit this knowledge (i.e., not just recovering text fragments or URLs) in automated processes, it is necessary to process the text and extract the pieces of information in a semantic format useful for machine analysis.


In the eHealth-KD challenge context, the selection of which algorithms and hyper-parameters to use for each subtask can be framed as a classic AutoML problem.

there are additional high-level decisions, such as whether to solve subtasks sequentially or combined, that cannot be easily represented in traditional AutoML frameworks.


As a case study, we apply our proposal to the eHealth-KD challenge, and achieve state-of-the-art results in one of the evaluation scenarios proposed.

this approach can be extended to other machine learning scenarios.


Furthermore, the challenge defines three evaluation scenarios in which different subtasks are considered.

for the purpose of this research, we focus on Scenario 1, which considers all three subtasks and is thus the most complete and challenging.


A total of 6 researchers submitted their approaches in the eHealth-KD challenge, of which 5 evaluate in Scenario 1.

one of these participants did not submit a description paper, hence, it will not be considered in this research.


Our proposal is based on AutoML, using the metaheuristic grammatical evolution (O'Neill and Ryan, 2001) for defining and exploring the space of solution for dealing with the Scenario 1 of the eHealth-KD challenge.

the optimization process is different to the traditional grammatical evolution formulation because our grammar involves both discrete and continuous parameters.


The performance  of a pipeline will depend, in general, of complex interactions between its components that are not completely captured in a simple linear regression model.

there is a large correlation between feature weights and pipeline performance, as demonstrated by the coefficient of determination (R 2 = 0.763) of the regression.


Concretely, we first Then our attention mechanism models (trigger, context word) pair's relevance with a Multi-Layer Perceptron (MLP), and uses a softmax function normalizing relevance scores to attention weights: Given the attention weights, the lexical-specific context representation is summarized as c 0 = And the final lexical-specific representation of instance x is the concatenation of its token representation h 0 and the lexical-specific context representation c 0 , i.e., The lexical-specific representation can effectively disambiguate trigger words by capturing (trigger, context word) associations.

this representation is lexical-specific, thus hard to generalize well to sparse/unseen words.


Then the PCNNs model (Zeng et al., 2015) designed the multi-instance learning paradigm for RE.

the PCNNs model suffers the issue of the selection of sentences.


Graph Convolution Networks (Kipf and Welling, 2016;Schlichtkrull et al., 2017) and Graph attention networks (Veličković et al., 2017) also learn neighborhood based representations of nodes.

they do not learn a query-dependent composition of the neighborhood which is sub-optimal as also seen our in experiments and noted previously (Dettmers et al., 2017).


2016proposed a neighborhood mixture model which is closely related.

their proposed model learns a fixed mixture over neighbors as opposed to learning an adaptive mixture based on the query, and requires storing an embedding parameter for every entity-relation pair which can be prohibitively large, potentially O(V e × V r ) whereas our model only requires O(V e + V r ).


contribution as a synthesizer and that he is an instrumentalist for Keyboards to infer that he is a musician.

for queries like nationality, the model attends to neighbors like place of birth (see query for Burt Young) and places lived.


On the one hand, while syntactic information (i.e., the dependency tree) can directly connect "will" to "go", it will also promote some noisy words (i.e., "back") at the same time due to the direct links (see the dependency tree in Figure 1).

while deep learning models with the sequential structure can help to downgrade the noisy words (i.e., "back") based on the semantic importance and the close distance with "go", these models will struggle to capture "will" for the factuality of "go" due to their long distance.


, h n ), it is possible to use the hidden vector corresponding to the anchor word h k as the features to perform factuality prediction (as done in (Rudinger et al., 2018)).

despite the rich context information over the whole sentence, the features in h k are not directly designed to focus on the import context words for factuality prediction.


Since this distant data is created using rulebased classifiers, given a large amount of training data, the baseline model can achieve high performance as it learns to infer these rules.

our aim is to improve the performance of the event ordering model on moderately sized datasets, where the knowledge induction from timex embeddings play a larger role.


This demonstrates that the temporal model does not use the knowledge from time expressions when making temporal relation predictions.

in the ELMo setting, we observed a larger drop in performance by masking out the time expressions compared to GloVe embeddings.


Differentiable Neural Computer (DNC)  extends the NTM to address the issue by introducing a temporal link matrix, replacing the least used memory when the memory is full.

this method is a rule-based one that cannot maximize the performance on a given task.


TriviaQA), storing the evidence sentences that precede span words may be useful as they may provide useful context.

using only discrete policy gradient method, we cannot preserve such context instances.


ing the best accuracy, which may be due to its ability to capture global relative importance of each memory entry.

the gap between EMR-Transformer and EMR-biGRU diminishes as the size of memory increases, since then the size of the memory becomes large enough to contain all the frames necessary to answer the question.


With treebank data such as Penn Treebank (Marcus et al., 1993), we have pairs of (x, y) to train the two components respectively, and get high-quality language model with the parser providing grammar knowledge.

due to the expensive cost of accurate parsing annotation, Figure 1: Overall framework of neural variational language model (NVLM).


Subsequent work has homed in on language modeling (LM) pretraining, finding that such mod-els can be productively fine-tuned on intermediate tasks like natural language inference before transferring to downstream tasks (Phang et al., 2018).

we identify two open questions: (1) How effective are tasks beyond language modeling in training reusable sentence encoders (2) Given the recent successes of LMs with intermediate-task training, which tasks can be effectively combined with language modeling and each other.


Multitask pretraining or intermediate task training offers modest further gains.

we see several worrying trends: • The margins between substantially different pretraining tasks can be extremely small in this transfer learning regimen and many pretraining tasks struggle to outperform trivial baselines.


propose to firstly parse a question to a coarse logical form then a fine-grained one based on a neural architecture.

these approaches miss the opportunity to utilize question decomposition information for complex question semantic parsing.


The task involves assessing whether a given premise entails a given hypothesis.

to other entailment datasets mentioned previously, the hypotheses in SciTail are created from science questions while the corresponding answer candidates and premises come from relevant web sentences retrieved from a large corpus.


Dependency parsing allows us to design our extraction method such that each S1 and S2 is interpretable as a full sentence in isolation, and the appropriate conceptual relation holds between the pair.

occasionally we get ungrammatical sentences or the wrong pair of sentences for a relation.


We do not allow the classifier to access the underlying discourse relation type and we only provide the individual sentence embeddings as input features.

patterson and Kehler (2013) used a variety of discrete features provided by the pDTB dataset for their classifier, including the hand-annotated relation types.


Adopted by several works later on (Miller et al., 1996;Zettlemoyer and Collins, 2009;Suhr et al., 2018), ATIS has only a single domain for flight planning which limits the possible SQL logic it contains.

to ATIS, SParC consists of a large number of complex SQL queries (with most SQL syntax components) inquiring 200 databases in 138 different domains, which contributes to its diversity in query semantics and contextual dependencies.


In comparison, SParC is significantly different as it (1) contains mode complex contextual dependencies, (2) has greater semantic coverage, and (3) adopts a cross-domain task setting, which make it a new and challenging cross-domain contextdependent text-to-SQL dataset.

sParC has overcome the domain limitation of ATIs by covering 200 different databases and has a significantly larger natural language vocabulary.


The domain by its nature requires the user to express multiple constraints in separate utterances and the user is intrinsically motivated to interact with the system until the booking is successful.

the interaction goals formed by Spider questions are for open-domain and general-purpose database querying, which tend to be more specific and can often be stated in a smaller number of turns.


It can be seen that the DCTbased embedding creation technique needs more components to achieve reasonable performance, as compared to PCA, because PCA learns the basis vectors in a data-driven way while DCT assumes cosine functions as bases.

since it does not need to learn the bases, and therefore makes less errors than PCA, DCT is more performant than PCA when we utilize more components.


The human performance numbers in Table 1 shows that overall our annotators stick it to the Muppets on GLUE.

on MRPC, QQP, and QNLI, Bigbird and BERT outperform our annotators.


In the first example, the model originally (incorrectly) assumes that "Toronto" refers to the city, tagging it as a GPE.

after resolving the semantic role -determining that "Toronto" is the thing getting "smoked" (ARG1) -the entity-typing decision is revised in favor of ORG (i.e.


In the second example, the model initially tags "today" as a common noun, date, and temporal modifier (ARGM-TMP).

this phrase is ambiguous, and it later reinterprets "china today" as a proper noun (i.e.


As with BERT, we observe that the weights are only weakly concentrated for the relations and SPR tasks.

unlike BERT, we see only a weak concentration in the weights on the coreference task, which agrees with the finding of Tenney et al.


The BiLSTM layer in the baseline and our model is capable of capturing high-order information to some extent.

without prior knowledge of high-order parts, it may require more training data to learn this capability than a high-order decoder.


Mean field variational inference slows down training and parsing by 35% and 20% respectively compared with the baseline.

loopy belief propagation slows down training and parsing by 65% and 67% respectively compared with the baseline.


(2016b) proposed a dataset similar to us, i.e., based on the TV show Friends.

their corpus only includes the textual modality and is thus not multimodal in nature.


Due to the speaker overlap across splits, the model can leverage speaker regularities for sarcastic tendencies.

we do not observe the same trend for the best multimodal variant (text + video) where the score barely improves.


(2017), many sentences either do not express an argument or cannot be understood out of context.

our dataset explicitly provides the sequence of claims in an argument path that leads to any particular claim, which can enable an argument generation system to generate relevant claims, with a particular stance and at the right level of specificity.


Some of these studies have shown that simple linear classifiers with uni-gram and n-gram features are effective for this task (Somasundaran and Wiebe, 2010;Hasan and Ng, 2013;Mohammad et al., 2016).

in our setting, since we try to predict the stance between all pairs of claims on an argument path, rather than simply claims that are directed towards the thesis or the parent claim, we find that the models with a hierarchical representation of the argument path, i.e.


These models handle sentiment composition implicitly and predict sentiment polarities only based on embeddings of current nodes.


The model includes a bidirectional LSTM encoder and a unidirectional LSTM decoder with attention, and only the encoder is used for downstream task-specific models.

pre-training is limited by the availability of parallel corpora.


We are surprised to find that BERT's peak performance of 77% on the Argument Reasoning Comprehension Task reaches just three points below the average untrained human baseline.

we show that this result is entirely accounted for by exploitation of spurious statistical cues in the dataset.


To answer our question in the introduction: BERT has learned nothing about argument comprehension.

our investigations confirmed that BERT is indeed a very strong learner.


These reasons are informative about which stance is taken, because they give more details on how a stance is developed.

simply comparing the reasons may not be sufficient to predict stance (dis)agreement, as sometimes people can take the same stance but give different reasons (e.g., the points outlaws having guns and freedom of speech mentioned in Table 1).


This indicates that the refined representation is more learnable and is able to extract the interdependence between aspect and the corresponding target in the context.

the performance of sentiment classification is improved certainly in comparison with the remarkable models (Delayed-memory and SenticLSTM).


Although the neural network-based approach (Potash et al., 2017;Eger et al., 2017) achieves high performances for ASP, it does not explicitly take into account useful linguistic clues.

prior works demonstrate that linguistic features, particularly discourse connectives, are strong clues to predict the structure for ASP (Lawrence and Reed, 2015;Stab and Gurevych, 2017).


The given input is a paragraph consisting of T tokens w 1: the other reason is that consequently, in conclusion, from the above views, although first, as you can see that in this essay , the reasons for why i agree that it is a debatable subject that unfortunately although some argue that furthermore , it 's undeniable that the in short, another thing that put big cities in front of small towns is in conclusion, despite the contribution of it to the society, however, some say that  Some people may argue that (AM1) children will be more material, neglect their study for earning money or be exploited by the employers(AC1).

(AM2) if children get good care and instructions from their parents, they can take advantages of the work to learn valuable things and avoid going in a wrong way(AC2).


Instead of making a prediction based on the comparison result of a single alignment process, a stacked model with multiple alignment layers maintains its intermediate states and gradually refines its predictions.

suffering from inefficient propagation of lower-level features and vanishing gradients, these deeper architectures are harder to train.


The simpler implementation of the fusion layer leads to evidently worse performance, indicating that the fu-   sion layer cannot be further simplified.

the alignment layer and the prediction layer can be simplified on some of the datasets.


While translational models learn embeddings using simple operations and limited parameters, they produce low quality embeddings.

cNN based models learn more expressive embeddings due to their parameter efficiency and consideration of complex relations.


In contrast, CNN based models learn more expressive embeddings due to their parameter efficiency and consideration of complex relations.

both translational and CNN based models process each triple independently and hence fail to encapsulate the semantically rich and latent relations that are inherently present in the vicinity of a given entity in a KG.


Our model achieves these objectives by assigning different weight mass (attention) to nodes in a neighborhood and by propagating attention via layers in an iterative fashion.

as the model depth increases, the contribution of distant entities decreases exponentially.


One of the main capabilities of these models is that they implicitly exploit entailment relations such as person born in country entails person be from country (Riedel et al., 2013).

entailment relations are not learned explicitly.


This would suggest we can define a new link prediction score based on entailment relations: S ent q,e 1 ,e 2 = max r∈R: r→q S r,e 1 ,e 2 .

since we do not have access to the entailment relations and can only rely on the predictions, Equation 3 is likely to be very noisy.


This is because the other baselines have also access to the whole set of triples ( §4.4).

for evaluating the link prediction model, we compute entailment scores by only considering the predictions in the training set.


Deudon (2018) build a sentence-reformulating deep generative model whose objective is to measure the semantic similarity between a sentence pair.

their work cannot be applied to a multi-class classification problem, and the generative objective is only used in pre-training, not considering the joint optimization of the generative and the discriminative objective.


As described by Glavaš andŠnajder (2014a), there have been efforts that have focused on detecting temporal and spatial subevent containment individually.

it is clear that subevent detection requires both simultaneously.


While ActivityNet starts o↵ harder for BERT (25.5%), it also proves di cult for humans (60%).

wikiHow starts easier for BERT (41.1%) and humans find the domain almost trivial (93.5%).


Recently, there has been a surge in neural encoderdecoder techniques which are trained with input utterances and corresponding annotated output programs (Dong and Lapata, 2016;Jia and Liang, 2016).

the performance of these strongly supervised methods is restricted by the size and the diversity of training data i.e.


Herzig and Berant (2017) propose semantic parsing models using supervised learning in a multi-domain setup and is the closest to our work.

none of the existing works inspect the problem of multi-domain semantic parsing in a weak supervision setting.


These are the two ways for the log-loss approach to make predictions with high accuracy: always giving very high score for the entailment hypothesis and low score for the contradiction hypothesis, but giving either very high or very low score for the neutral hypothesis.

the margin-loss gives more intuitive scores for these two examples.


1 Conceptually, GLEN is similar to the explicit retrofitting model of , who focus on the symmetric semantic similarity relation.

gLEN has to account for the asymmetric nature of the LE relation.


This reliance on attention may lead one to expect decreased performance on commonsense reasoning tasks (Roemmele et al., 2011;Zellers et al., 2018) compared to RNN (LSTM) models (Hochreiter and Schmidhuber, 1997) that do model word order directly, and explicitly track states across the sentence.

the work of (Peters et al., 2018a) suggests that bidirectional language models such as BERT implicitly capture some notion of coreference resolution.


That is, they are unable to resolve pronouns in light of abstract/implicit referrals that require background knowledge -see (Saba, 2018) for more detail.


Neural networks have proven highly effective in natural language processing (NLP) tasks, outperforming other machine learning methods and even matching human performance (Hassan et al., 2018;Nangia and Bowman, 2018).

supervised models require many per-task annotated training examples for a good performance.


For the seq2seq-T (S2S-T) model (Qin et al., 2018), the comment is generated mainly based on the clue "ancient costume" in the title.

because "ancient costume" is not frequently seen in the comments (in the training set).


• Our CFNet is significantly better than two baselines (PGNet, NQG), our MSNet, and our CorefNet.

the difference between our CFNet and our FlowNet is not significant.


Neural models for automatic question generation using the standard sequence to sequence paradigm have been shown to perform reasonably well for languages such as English, which have a large number of training instances.

large training sets are not available for most languages.


In preliminary experiments, we explored a learned policy for operator selection.

we observed that the learned policy quickly collapses to a nearly deterministic choice of Rep φ 3 .


(2017) proposed to use an auxiliary model, trained to extract structured records from text, for evaluation.

the extraction model presented in that work is limited to the closed-domain setting of basketball game tables and summaries.


DROP is far from all datasets because it requires quantitative reasoning that is missing from other datasets.

it is relatively close to HOTPOTQA and WIKIHOP, which target multi-  Figure 1: A 2D-visualization of the similarity between different datasets using the force-directed placement algorithm.


(2018) generate explanations and predictions for the natural language inference problem (Camburu et al., 2018).

the authors report that interpretability comes at the cost of loss in performance on the popular Stanford Natural Language Inference (Bowman et al., 2015) dataset.


(2018) use human explanations to train a neural network model on the SNLI dataset (Bowman et al., 2015).

they obtain explanations at the cost of accuracy.


We only present annotators with query instances for which both models output the same answer.

we do not restrict these answers to be the ground truth.


Given a pair of words (i, j) and associated word vectors , we compute the similarity between two words by their vector similarity.

we encode this similarity in a weighted adjacency matrix A: nodes are only connected to their knearest neighbors (Section 6.2 examines the sensitivity to k); all other edges become zero.


For ZH, HI, and KO, the improvement comes from selecting better mappings during the adversarial step.

modularity does not improve on all languages (e.g., VI) that are reported to fail by Hoshen and Wolf (2018).


Our results suggest that simple properties of a language-the word inventory and (to a lesser extent) the raw character sequence length-are statistically significant indicators of modeling difficulty within our large set of languages.

we fail to reproduce our earlier results from Cotterell et al.


As Figure 2 shows, the number of merges that maximizes log-likelihood of our dev set differs from language to language.

8 as we will see in Figure 3, tuning this parameter does not substantially influence our results.


Because it is multiplicative, Model 1 appropriately predicts that in each language , intents with large will not only have larger values but these values will vary more widely.

model 1 is homoscedastic: the variance 2 of log( / ) is assumed to be independent of the independent variable , which predicts that the distribution of should spread out linearly as the information content increases: e.g., ( That assumption is questionable, since for a longer sentence, we would expect log / to come closer to its mean as the random effects of individual translational choices average out.


Speakers prefer shorter dependencies in both production and processing, and average dependency lengths tend to be much shorter than would be expected from randomly-generated parses (Futrell et al., 2015;Liu et al., 2017).

there is substantial variability between languages, and it has been proposed, for example, that head-final languages and case-marking languages tend to have longer dependencies on average.


We did implement this model and test it on data, but not only was fitting it much harder and slower, it also did not yield particularly encouraging results, leading us to omit it from the main text.

c Goodness of fit of our difficulty estimation models Figure 6 shows the log-probability of held-out data under the regression model, by fixing the estimated difficulties (and sometimes also the estimated variance 2 ) to their values obtained from training data, and then finding either MAP estimates or posterior means (by running HMc using STAN) of 29 version (a) is then deficient since it then incorrectly allocates some probability mass to + < 0 and thus < 0 is possible.


This provides clear evidence of M-BERT's multilingual representation ability, mapping structures onto new vocabularies based on a shared representation induced solely from monolingual language model training data.

cross-script transfer is less accurate for other pairs, such as English and Japanese, indicating that M-BERT's multilingual representation is not able to generalize equally well in all cases.


This provides further evidence that M-BERT uses a representation that is able to incorporate information from multiple languages.

m-BERT is not able to effectively transfer to a transliterated target, suggesting that it is the language model pre-training on a particular language that allows transfer to that language.


Understanding the expression of complaints in natural language and automatically identifying them is of utmost importance for: (a) linguists to obtain a better understanding of the context, intent and types of complaints on a large scale; (b) psychologists to identify human traits underpinning complaint behavior and expression; (c) organizations and advisers to improve the customer service by identifying and addressing client concerns and issues effectively in real time, especially on social media; (d) developing downstream natural language processing (NLP) applications, such as  dialogue systems that aim to automatically identify complaints.

complaining has yet to be studied using computational approaches.


To the best of our knowledge, the only previous work that tackles a concept defined as a complaint with computational methods is by Zhou and Ganesan (2016) which studies Yelp reviews.

they define a complaint as a 'sentence with negative connotation with supplemental information'.


to intensifiers, downgrading modifiers are used to reduce the face-threat involved when voicing a complaint, usually as part of a strategy to obtain a reparation for the breach of ex-pectation (Meinl, 2013).


Several unigrams (error, issue, working, fix) and a cluster (Issues) contain words referring to issues or errors.

words regularly describing negative sentiment or emotions are not one of the most distinctive features for complaints.


The topical features such as the LIWC dictionaries (which combine syntactic and semantic information) and Word2Vec topics perform in the same range as the part of speech tags.

best predictive performance is obtained using bag-of-word features, reaching an F1 of up to 77.5 and AUC of 0.866.


The former one aims to generate single-token answers from automatically constructed pseudo-questions while the latter requires choosing from multiple answer candidates.

such unnatural settings make them fail to serve as the standard QA benchmarks.


Since the tweet contexts are short, there are only a small number of named entities to choose from, which could make the answer pattern easy to learn.

the neural models fail to perform well on the "Why" questions, and the results of neural baselines are even worse than that of the matching baseline.


Some researches (Heilman and Smith, 2010;Figueroa and Neumann, 2013) directly trained question ranking (QR) models via supervised learning, and used it to perform evaluation.

these models are always domainspecific and not interpretable since we cannot tell what makes a question get a high (low) score.


By using the "expected value of perfect information", they proposed a useful evaluation model.

our task significantly differs from it in two aspects: first, there is no correct answer for open-answered questions thus it is hard to tell which answer is "useful".


As mentioned above, if we control ∆t to make D rather small, the effect of time will be greatly reduced.

we may filter out too many data if making ∆t too close to 0.


As nouns are often topic words (occurred with adjectives and prepositions), it is better to contain less topics and ask one thing a time.

it is better to use more verbs and adverbs to make the question vivid.


When features in Section 3.2 are not used, the CNN model gets the best performance, which is not surprised.

adding these features greatly improves the performance of all statistical models, making SVM and RF significantly surpass CNN.


Sequential classifiers like LSTMs are good at learning temporal structure and are biased to use prior inputs to predict outputs (Eck and Schmidhuber, 2002).

when it comes to comparison tasks like stance classification in threaded discussions, each reply is made against a post or a response to a source post (see Fig.


Using an operation that reduces the children hidden layer matrix h to fixed dimension vector like in equation 8 or in equation 10 attempts to solve the problem.

these reduction operators have limitations e.g.


Unfortunately, the few existing human judgment datasets have been created as by-products of the manual evaluations performed during the DUC/TAC shared tasks.

modern systems are typically better than the best systems submitted at the time of these shared tasks.


It is worth remembering that JS-2 and R-2 both operate on bigrams which also explain their stronger connection.

in the high-scoring range (T), correlations are low and often negative.


The specificities of each metric are averaged-out putting the focus on the general trend.

when the metrics do not correlate, improvements according to one metric are rarely improvements for the other ones.


Intuitively, smaller populations and narrow scoring ranges can also lead to lower correlations.

(T) displays low correlations with 102 summaries per topic whereas (A) has strong correlations with 50 summaries per topic.


Our analysis is performed on TAC-2008 and TAC-2009 because they are benchmark datasets typically used for comparing evaluation metrics.

our approach can be applied to any dataset.


Across domains content overlap (R1) is˜50 points.

r2 is much lower indicating that there is abstraction, paraphrasing, and content selection in the summaries with respect to the input.


More irregular forms will tend to have a lower wug-test probability P(w | , σ, L − ) than most regular forms.

the absolute value of such a probability is not directly interpretable.


Many inflectional systems display syncretismthe morphological phenomenon whereby two slots with distinct morpho-syntactic tags may have an identical surface form.

to many models of inflectional morphology, we collapse syncretic forms of a word into a single paradigm slot, thereby assuming that every every surface form w in a paradigm is distinct.


As discussed above, we hold out whole lexemes, including all of their inflected forms during training.

derivational morphology presents a potential challenge for this approach.


Although this sample represents a reasonable degree of typological diversity, the Indo-European family is overrepresented in the UniMorph dataset, as is the case for most current multilingual corpora.

within the Indo-European family, we consider a diverse set of subfamilies: Albanian, Armenian, Slavic, Germanic, Romance, Indo-Aryan, Baltic, and Celtic.


The rate gate r controls how slow and fast-moving memory states are mixed inside the model.

to the model originally trained in Ororbia II et al.


"A dog" vs. "The dog" For the PassAct2 dataset, we observe that determiner information is retained well by most layers.

the shallow layers retain information better than the deeper layers.


"The happy child" vs. "The child" For the Act3 dataset, we observe that middle layers of most models (BERT, Multitask) retain the adjective information well.

surprisingly simple multitask model (lstm 1 forward layer accuracy = 0.89) retains adjective information better than BERT model (layer 7 accuracy = 0.84).


In logical negation, the negation operator has been succinctly described as a truth-functional operation, reversing the truth value of a sentence.

from a pragmatic point of view, the primary function of negation is to direct attention to an alternative meaning and can thus be, more generally, compared to our ability for counterfactual thinking (Hasson and Glucksberg, 2006).


For exam-ple, negation of action-related sentences or imperatives involves decreased activity in motor systems of the brain implicated in action semantics when compared to the affirmative context (Tettamanti et al., 2008;Tomasino et al., 2010).

overall reduced activation does not necessarily equate to a lack of information across patterns of activated or deactivated voxels in a brain region (Kriegeskorte et al., 2008).


Similarly, the Aff Verbs neural estimates showed significant correlations with the VERB model in the LPG (r = 0.04, p < 0.01), LIFG (r = 0.05, p < 0.01) and not the LMTP.

we did not find that Neg Verbs triggered any significant correlations with the VERB model in the ROIs tested.


This may provide a 'default' negation meaning (Papeo et al., 2016), as well as allow competing or cooperating semantic alternatives to emerge.

it is also possible that the results reflect a more 'categorical' representation of negation and that the current semantic models are merely not a suitable represenation for the negated meaning.


The noniconic language without markers is the most difficult to learn, as expected.

in listening mode we encounter again a preference for backward iconicity.


In listening mode, this agent shows the expected preference for markers in the free-order case (as the free-order language without markers is massively ambiguous, with most utterances mapping to multiple trajectories).

among the fixed-order languages, both backward and noniconic prefer redundant coding.


For fixed-order languages, we do not observe any change in accuracy or behavior in the listener direction (the last-generation child is perfectly parsing the initial language).

we observe in speaker mode a (relatively small) decrease in accuracy across generations, which, importantly, affects the most natural language (forward iconic without markers) the least, and the most difficult language (non-iconic without markers) the most (results are in Supplementary).


It is used for many downstream language processing tasks, e.g., coreference resolution, question answering, summarization, entity linking, relation extraction and knowledge base population.

most NER tools are designed to capture flat mention structure over coarse entity type schemas, reflecting the available annotated datasets.


Besides, Muis and Lu (2017) developed a gap-based tagging schema to capture nested structures.

these schemas should be designed very carefully to prevent spurious structures and structural ambiguity .


Methods from the first category focus on encoding high-order network interactions in a scalable fashion, such as LINE (Tang et al., 2015), DeepWalk (Perozzi et al., 2014).

models based on topological embeddings alone often ignore rich heterogeneous information associated with the vertices.


Usually, the model would take the risk of over-fitting if it is only optimized upon the target attributes due to unavoidable noises in the dataset.

the Multi-task learning implicitly increases training data of other relevant tasks having different noise patterns and can average these noise patterns to obtain a more general representation and thus improve generalization of the model.


The supervised approaches treat keyphrase extraction as a binary classification task, in which a learning model is trained on the features of labeled keyphrases to determine whether a candidate phrase is a keyphrase (Witten et al., 1999;Medelyan et al., 2009;.

the unsupervised approaches directly treat keyphrase extraction as a ranking problem, scoring each candidate using different kinds of techniques such as clustering (Liu et al., 2009), or graph-based ranking (Mihalcea and Tarau, 2004;Wan and Xiao, 2008).


(2019) proposed a title-guided Seq2Seq network to use title of source text to improve performance.

these methods did not consider the linguistic constraints of keyphrases.


Early detection and monitoring of adverse drug reactions (ADRs) can minimize the deleterious impact on patients and health-care systems (Hakkarainen et al., 2012;Sultana et al., 2013 For prevention, the drug safety organizations known as pharmacovigilance agencies conduct post-market surveillance to identify the drug's side effects post-release.

the majority of the existing ADE surveillance systems utilizes passive spontaneous reporting system databases, such as the Federal Drug Administration's Adverse Event Reporting System (FAERS) (Li et al., 2014).


This may correspond to mapping previously undiscovered adverse effect with a given drug, or discovering an unforeseen impact to a change in the manufacturing process.

extracting this information from the unstructured text poses several challenges as follows: • Multiple Context: Context carries an essential role in determining the semantic labels of the medical concepts.


Further works (Gurulingappa et al., 2012b;Benton et al., 2011;Harpaz et al., 2012a) utilized the lexicon-based approach to extract the ADRs.

these approaches are only restricted to a number of target ADRs.


For e.g., in the sentence-2 of Table-4, here 'could not sleep', 'move it behind my back' is also an ADR, in addition to 'pain in upper right arm'.

the first two ADRs are not labeled in the dataset.


According to empirical results illustrated in Table 2, our approach is able to give more accurate answers to multi-step problems, while the accuracy of single-step problems is lower.

three models have similar patterns in terms of performance for three types of signs.


as shown in Figure 1, as more labels are removed, the advantage of Seq2Set over Seq2Seq continues to grow.


An updated, larger b kt will lead to a larger agreement value c kt between the t-th word and the k-th slot in the next iteration.

it assigns low c kt when there is inconsistency between p k|t and v k .


(2018) utilize a slot-gated mechanism as a special gate function in Long Short-term Memory Network (LSTM) to improve slot filling by the learned intent context vector.

as the sequence becomes longer, it is risky to simply rely on the gate function to sequentially summarize and compress all slots and context information in a single vector (Cheng et al., 2016).


An advantage of these approaches is that the aspect or opinion terms whose usage in a sentence follows some certain patterns can always be extracted.

it is labor-intensive to design rules manually.


Topic modeling approaches (Lin and He, 2009;Brody and Elhadad, 2010;Mukherjee and Liu, 2012) are able to get coarse-grained aspects such as food, ambiance, service for restaurants, and provide related words.

they cannot extract the exact aspect terms from review sentences.


Dataset #ATER #OTER #EAT #EOT SE14-R  431  618  1,453 1,205  SE14-L  157  264  670  665  SE15-R  133  193 818 578  Compared with the other approaches, RI-NANTE only fails to deliver the best performance on the aspect term extraction part of SE14-L and SE15-R. On SE14-L, DE-CNN performs better.

our approach extracts both aspect terms and opinion terms, while DE-CNN and HAST only focus on aspect terms.


On the other hand, rewriting the adjunct tokens can smooth the generated data and expand their diversity.

since there is no explicit guide, this step can also introduce unpredictable noise, making the generation not fluent as expected.


2 Data-driven end-to-end models trained on that dataset could implicitly bias towards predicting PERSON for most occurrences of Clinton even under some contexts when it refers to a location.

for frequently studied languages such as English, people have already collected dictionaries or lexicons consisting of long lists of entity names, known as gazetteers.


In the online setting, at each iteration k, only the k + 1th column of table T needs to be evaluated.

to Lemma 1 in the offline setting, where only the elements in the k + 1-th column below index k need to be computed, all elements of the k + 1-th column need to be evaluated in the online setting.


The term α k−1 k,k+1 is whatever character is on the transition from state k to k + 1.

α k−1 k+1,k is the set of paths that take state k+1 to state k without passing through states higher than k. Lemma 5.


The previous methods that introduce additional operations cannot adopt such parser directly.

although post-processing approach can use any parser in pre-processing, our approach outperforms the post-processing approach, even if the pre-processing parser is assumed to always generate gold PTB trees.


(2013) first compute the n best constituency trees using a probabilistic context-free grammar, convert those into dependency trees using a dependency model, compute a probability score for each of them, and finally rerank the most plausible trees based on both scores.

these methods are complex and intended for statistical parsers.


Attention mechanism is firstly applied in Computer Vision (CV)  where pixels are the basic units.

in NLP, the minimum unit is not word but sense.


Normally, various aspects of semantics are entangled in word embeddings (Bengio et al., 2003;Mikolov et al., 2013).

only some of the aspects are needed in specific tasks and other redundant aspects can be regarded as noise.


(2016) proposed a novel type of hard attention and apply it to improve the interpretability of models.


11 https://www.tensorflow.org/hub 12 Note that the encoder architectures similar to the ones used by USE can also be used in the Reddit pretraining phase in lieu of the architecture shown in Figure 2.

the main goal is to establish the importance of target response selection fine-tuning by comparing it to direct application of state-of-the-art pretrained encoders, used to encode both input and responses in the target domain.


The comparison of two fine-tuning strategies suggests that the simpler FT-DIRECT fine-tuning has an edge over FT-MIXED, and it seems that the gap between FT-DIRECT and FT-MIXED is larger on bigger datasets.

as expected, FT-DIRECT adapts to the target task more aggressively: this leads to its degraded performance on the general-domain Reddit response selection task, see the scores in parentheses in Table 3.


Humans provided instructions only about half of the time, and devoted more energy to providing higher-level descriptions of the target, responding to the Builder's actions and queries, and rectifying mistakes.

even the improved model failed to capture this, mainly generating instructions even if it was inappropriate or unhelpful to do so.


With such a memory mechanism, SLU model can retrieve context knowledge to reduce the ambiguity of the current utterance, contributing to a stronger SLU model.

the memory consolidation, a well-recognized operation for maintaining and updating memory in cognitive psy-  chology (Sternberg and Sternberg, 2016), is underestimated in previous models.


This dataset was collected with the Wizardof-Oz scheme (Wen et al., 2017) and consists of 3,031 multi-turn dialogues in three distinct domains, and each domain has only one intent, including calendar scheduling, weather information retrieval, and point-of-interest navigation.

all dialogue sessions of KVRET are single domain.


We design the turn-level attention to score the dialogue turns explicitly, so the more salient turns will obtain higher scores, which is similar to (Hsu et al., 2018).

instead of calculating the sentence-level attention using a separate recurrent component, we directly obtain the turn representations H turn by collecting hidden states from H with the turn-level segment position indices where m is the turn number of the dialogue content.


Turn Segmentation: In a smooth conversation, one turn is an adjacency pair of two utterances from two speakers (Sacks et al., 1974).

in real scenarios, the conversation flow is often disrupted by verbal distractions such as interlocutor interruption, back-channeling, self-pause and repetition (Schlangen, 2006).


Natural language understanding is to extract the core semantic meaning from the given utterances, while natural language generation is opposite, of which the goal is to construct corresponding sentences based on the given semantics.

such dual relationship has not been investigated in literature.


Natural language has the intrinsic sequential structure and temporal dependency, so modeling the joint distribution of words in a sequence by such autoregressive property is logically reasonable.

slot-value pairs in semantic frames do not have a single directional relationship between them, while they parallel describe the same sentence, so treating a semantic frame as a sequence of slot-value pairs is not suitable.


With the rise of deep learning approaches, several neural belief trackers (NBT) have been proposed and improved the performance by learning semantic neural representations of words Mrkšić and Vulić, 2018).

the scalability still remains as a challenge; the previously proposed methods either individually model each domain and/or slot (Zhong et al., 2018;Ren et al., 2018;Goel et al., 2018) or have difficulty in adding new slot-values that are not defined in the ontology Nouri and Hosseini-Asl, 2018).


The generative method  try to generate positive and negative examples from known classes by using adversar-ial learning to augment training data.

the method does not work well in the discrete data space like text, and a recent study (Nalisnick et al., 2019) suggests that this approach may not work well on real-world data.


It's practical that a conversation takes place under a background, meanwhile, the query and response are usually most related and they are consistent in topic but also different in content.

little work focuses on such hierarchical relationship among utterances.


we assume that the probability of the output y is conditioned on a latent tree T ∈ T (x), a variable that is not observed during training: it must be inferred from the data.


Our latent dependency model slighty improves (+0.8) over the CoreNLP baseline.

we observe that while our baseline is better than the one of Niculae et al.


(2019) proposed an aggressive inference network training.

their training algorithms are computationally expensive since they require backpropagating through the decoder or the encoder multiple times.


A successful partial-input baseline indicates that a dataset contains artifacts which make it easier than expected.

examples where this baseline fails are "hard" (Gururangan et al., 2018), and the failure of partial-input baselines is considered a verdict of a dataset's difficulty (Zellers et al., 2018;Kaushik and Lipton, 2018).


Each combination uniquely identifies a label, e.g., A in the premise and B in the hypothesis equals Entailment.

a single code word cannot identify the label.


Figure 2 shows the BLEU scores on IWSLT De→En dataset of each method, from which we can see that our method can observe a consistent BLEU improvement within a large probability range and obtain a strongest performance when γ = 0.15.

other methods are easy to lead to performance drop over the baseline if γ > 0.15, and the improvement is also limited for other settings of γ.


It turns out that in all but one case, the more words, the better the performance.

we did not get statistically significant results with class descriptors with dimensions higher than 100.


As shown, the gaps of success rates between the models are not very large, because all models can give pretty high success rate.

as expected, our proposed MHA provides lower perplexity (PPL) 1 , which means the examples generated by MHA are more likely to appear in the corpus of the evaluation language model.


Existing approaches for explainable machine learning systems tend to focus on interpreting the outputs or the connections between inputs and outputs.


If our model categorizes A as "GOOD" and it tells that the quality of A is "HIGH", the practicality is "HIGH" and the price is "LOW", we can regard these values of attributes as good explanations that illustrate why the model judges A to be "GOOD".

if our model produces the same values for the attributes, but it tells that A is a "BAD" product, we then think the model gives bad explanations.


For example, given a review sentence, "The product is good to use", we may not be sure if the product should be rated as 5 stars or 4 stars.

if we see that the attributes of the given product are all rated as 5 stars, we may be more convinced that the overall rating for the product should be 5 stars.


For all the interest in adversarial computer vision, these attacks are rarely encountered outside of academic research.

adversarial 1 All code for our defenses, attacks, and baselines is available at https://github.com/danishpruthi/ Adversarial-Misspellings Original A triumph, relentless and beautiful in its downbeat darkness + Swap A triumph, relentless and beuatiful in its downbeat darkness -Drop A triumph, relentless and beautiful in its dwnbeat darkness -+ Defense A triumph, relentless and beautiful in its downbeat darkness + + Defense A triumph, relentless and beautiful in its downbeat darkness + misspellings constitute a longstanding real-world problem.


Intuitively, one might suppose that word-piece and character-level models would be more robust to such attacks given they can make use of the remaining context.

we find that they are the more susceptible.


With evaluations on 14 treebanks, we empirically show that global output-structured models can generally obtain better performance, especially on the metric of sentence-level Complete Match.

probably because neural models already learn good global views of the inputs, the improvement brought by structured output modeling is modest.


Overall, the global models 4 perform better consistently, especially on the metrics of Complete Match, showing the effectiveness of being aware of global structures.

the performance gaps between global models and local models are small.


Several meta-analyses and empirical studies have shown the high efficacy and success of MI in psychotherapy (Burke et al., 2004;Martins and McNeil, 2009;Lundahl et al., 2010).

mI skills take practice to master and require ongoing coaching and feedback to sustain (Schwalbe et al., 2014).


The biggest improvements are for categorizing therapist codes (Table 9), especially for the RES and REC.

increasing the window size beyond 8 does not help to categorize client codes (Table 8) or forecasting (in appendix).


However, the knowledge is grounded in images.

we focus on knowledge grounded in videos, which is more complex, considering the large feature space spanning across multiple video frames and modalities that need to be understood.


Given the keyword Basketball of the current turn and its closeness score (0.47) to the target Dance, the only valid candidate keywords for the next turn are those with higher target closeness, such as Party with a closeness score of 0.62.

transitioning from Basketball to Sport is not allowed in the context as it does not move towards the target.


Traditional persuasive dialogue systems have been applied in different fields, such as law (Gordon, 1993), car sales (André et al., 2000), intelligent tutoring (Yuan et al., 2008).

most of them overlooked the power of personalized design and didn't leverage deep learning techniques.


Distinct-1 and distinct-2 are widely used in the literature (Li et al., 2016a;Shen et al., 2018a;Xu et al., 2018b), measuring the ratio of unique unigrams/bigrams to the total number of unigrams/bigrams in a set of responses.

they are very sensitive to the test data size, since increasing the number of examples in itself lowers their value.


Most metrics show a similar trend of increasing until 100k steps, and then stagnating (see Appendix A.3 for more figures).

validation loss for the same training reaches its minimum after about 10-20k steps ( Figure 5).


entropy, divergence, dis-tinct), randomly selected responses achieve similar values as the ground truth, which is expected.

on embedding metrics, coherence, and BLEU, random responses are significantly worse than those of any model evaluated.


These word embeddings were learned from unsupervised Neural Language Modelling (NLM) trained on fixed-length contexts.

by recasting the same word types across different sense-inducing contexts, these representations became insensitive to the different senses of polysemous words.


As we explained in the introduction, word embeddings are limited by meaning conflation around word types, and reduce NLM to fixed representations that are insensitive to contexts.

with fastText (Bojanowski et al., 2017) we're not restricted to a finite set of representations and can compositionally derive representations for word types unseen during training.


In a different line of work, Vilnis and McCallum (2015) proposed representing words as Gaussian distributions to embed uncertainty in dimensions of the embedding to better capture concepts like entailment.

athiwaratkun and Wilson (2017) argued that such a single prototype model can't capture multiple distinct meanings and proposed W ord2GM to learn multiple Gaussian embeddings per word.


The SCAS model simply uses the sum of all the sememes' embeddings of a constituent as the external information.

a constituent's meaning may vary with the other constituent, and accordingly, the sememes of a constituent should have different weights when the constituent is combined with different constituents (we show an example in later case study).


(2018) created graded metaphoricity layers for VUAMC using crowdsourcing, with the former approach labelling grammatical constructions and the latter labelling tokens.

manually labelling larger amounts of data is costly, even with crowdsourcing.


The differences caused by tied BWS scores do not indicate errors but show a small difference due to the nature of BWS and GPPL scores.

for metaphor novelty, the difference when tied scores are removed is negligible.


English is undoubtedly the most prominent language with multiple resources and tools.

english language processing is only a part of NLP in general.


The authors propose several probing tasks and divide them into those pertaining to surface, syntactic and semantic phenomena.

we decide to discard the 'syntactic versus semantic' distinction and consider all tasks either surface (see Section 3.1) or compositional (see Section 3.2).


Ideally, we would like to have linearly separable spaces with respect to S-classes -presumably embeddings from which information can be effectively extracted by such a simple mechanism are better.

this might not be the case considering the complexity of the space: non-linear models may detect S-classes more accurately.


This can be as straightforward as simply having a feature engineered baseline, as with our named entity recognition example (Section 4.2).

it can also be as nuanced as having access to an analysis of the types of required knowledge to accurately predict certain instances in the data, as in our recognizing textual entailment example where we use an alternate validation set for the MultiNLI corpus where subsets have been earmarked as of interest for specific kinds of task and linguistic knowledge (Section 4.1).


If the knowledge brought to the process is only partial, then only partial understanding of network function will be possible.

as one iterates through the interpretation process, the potential relevance of additional knowledge may emerge, and the process can be repeated with the expanded set.


The pathways were created by analyzing which neurons behave cohesively, indicating a subprocess within the network.

these subprocesses do not correspond strongly to any of the tested features.


For instance, in English or Norwegian we take [a] nap, whereas in Spanish we throw it, and in French, Catalan, German and Italian we make it.

they are compositionally less rigid than some other types of multiword expressions such as, e.g., idioms (as, e.g., [to] kick the bucket) or multiword lexical units (as, e.g., President of the United States or chief inspector).


We found that, despite this operation being the go-to representation for lexical relation modeling, concatenation works as well or better, and clear improvements can be obtained by incorporating explicitly learned relation vectors.

even with these improvements, categorizing LFs proves to be a difficult task.


The reason is that the quality of {Ŝ m H )L , T HE } is naturally restricted by the {Ŝ w H )L , T HE }, whose quality is in turn restricted by the induced dictionary.

by combining the augmented datasets from these two methods, we consistently improve the translation performance over using only word substitution augmentation (compare Table 2 rows 7 and 9).


Of course, we do not have enough s-t parallel data to obtain a good estimate of the true backtranslation distribution P s (X|y) (otherwise, we can simply use that data to learn θ).

we posit that even a small amount of data is sufficient to construct an adequate data selection policy Q(X|y) to sample the sentences x from multilingual data for training.


Automatic de-identification classifiers can significantly speed up the sanitization process.

obtaining a large and diverse dataset to train such a classifier that works well across many types of medical text poses a challenge as privacy laws prohibit the sharing of raw medical records.


In addition to structured medical data, EHRs contain free-text patient notes that are a rich source of information (Jensen et al., 2012).

due to privacy and data protection laws, medical records can only be shared and used for research if they are sanitized to not include information potentially identifying patients.


For values of N up to 1 000, our FastText and GloVe models are able to learn representations that allow training de-identification models that reach or exceed the target F 1 score of 95%.

training becomes unstable for N > 500: at this point, the adversary is able to break the representation privacy when trained for an additional 50 epochs (Fig.


FastText's approach to embedding unknown words (word embeddings are the sum of their subword embeddings) should intuitively prove useful on datasets with misspellings and ungrammatical text.

when using the additional casing feature, FastText beats GloVe only by 0.05 percentage points on the i2b2 test set.


Using the adversarially learned representation, de-identification models reach an F 1 score of 97.4%, which is close to the non-private system (97.67%).

the automatic pseudonymization approach only reaches an F 1 score of 95.0% at N = 500.


Named entity recognition (NER) is one of the best studied tasks in natural language processing.

most approaches are not capable of handling nested structures which are common in many applications.


If this is the case, then for the token "United", R and D will hold the embedding of/directions to the tokens "The" and "Kingdom" in their disaggregated (unmerged) form.

for the token "government", R and D will hold embeddings of/ directions to the combined entity "the United Kingdom" in each of the three slots for "The", "United" and "Kingdom".


The DL model improves dramatically as the data size increases and achieves the best performance among the 7 models when 7000 training examples are available.

the other models suffer much less from data scarcity with an exception of Random Forest.


Deep active learning (DAL) also outperforms SVM and yields comparable performance to DTAL.

the standard deviations in performance of DAL are substantially higher than those of DTAL (e.g.


F 1 score, the harmonic mean of precision and recall, is often used to select/evaluate the best models.

when precision needs to be prioritized over recall, a state-of-the-art model might not be the best choice.


Trade-offs between precision and recall have been well researched for classification (Joachims, 2005;Jansche, 2005;Cortes and Mohri, 2004).

barring studies on inference-time heuristics, there is limited work on training precision-oriented sequence tagging models.


(Pentina et al., 2015;Oh et al., 2015;Gong et al., 2016;Zhang et al., 2017)).

while in CL the prediction task is fixed but the trained algorithm is exposed to increasingly more complex training examples in subsequent stages, in TRL the algorithm is trained to solve increasingly more complex tasks in subsequent stages, but the training data is kept fixed across the stages.


Moreover, even the min values of RF2 consistently outperform the NoDA model (where a classifier is trained on the source domain and applied to the target domain without domain adaptation; bottom line of Table 1) and the min values of BasicTRL outperform NoDA in 5 of 6 setups (average difference of 3.9% for RF2 and for 3.5% for BasicTRL).

the min value of NoTRL is outperformed by NoDA in 5 of 6 cases (with an averaged gap of 2.8%).


To align the loss function with a perplexity measure we propose a simple refinement step, where we use the expected counts computed by the learned PNFA as features of a log-linear model, and learn interpolation weights.

to Equation 2, which uses the longest context x of length n to compute the conditional probability, the interpolated model leverages the ability of the PNFA to model substring expectations of all lengths up to n. This is similar to classic interpolation of language models (Rosenfeld, 1994;Chen, 2009).


Building a single model that jointly learns to perform many tasks effectively has been a longstanding challenge in Natural Language Processing (NLP).

multi-task NLP remains difficult for many applications, with multi-task models often performing worse than their single-task counterparts (Plank and Alonso, 2017;Bingel and Søgaard, 2017;McCann et al., 2018).


(2019) distill singlelanguage-pair machine translation systems into a many-language system.

they focus on multilingual rather than multi-task learning, use a more complex training procedure, and only experiment with Single→Multi distillation.


or formality (Fan et al., 2017;Hu et al., 2017;Ficler and Goldberg, 2017;Shen et al., 2017;Herzig et al., 2017;Fu et al., 2018;Rao and Tetreault, 2018).

human language actually involves a constellation of interacting aspects of style, and NNLG models should be able to jointly control these multiple interacting aspects.


Temp thinks white is going to win because white have the advantage of one more pawn.

temp cannot predict that white will lose the advantage in the next move.


Modeling human language requires the ability to not only generate fluent text but also encode factual knowledge.

traditional language models are only capable of remembering facts seen at training time, and often have difficulty recalling them.


We also employ the same regularization strategy (DropConnect (Wan et al., 2013) + Dropout (Srivastava et al., 2014)) and weight tying approach.

we perform optimization using Adam (Kingma and Ba, 2015) with learning rate 1e-3 instead of NT-ASGD, having found that it is more stable.


Although, learning a latent representation will make the model more interpretable and easy to manipulate, the model which is assumed a fixed size latent representation cannot utilize the information from the source sentence anymore.

there are also some approaches without manipulating latent representation are proposed recently.


They employ Denoising Auto-encoders (Vincent et al., 2008) and back-translation (Sennrich et al., 2016) to build a translation style between different styles.

both lines of the previous models make few attempts to utilize the attention mechanism to refer the long-term history or the source sentence, except Lample et al.


(6)), our model failed to learn a meaningful output and only learned to generate a single word for any combination of input sentence and style.

when we don't use cycle reconstruction loss (Eq.


However, if we only use the real sentence the model will lose a significant part of the ability to control the style of the generated sentence, and thus yields a bad performance in style accuracy.

the model can still perform a style control far better than the input copy model discussed in the previous part.


(2018b) is the only work on controlling the sentiment for story ending generation.

their work needs manually label the story dataset with sentiment labels (happy, sad, unknown), which is time-consuming and laborintensive.


Recently, many approaches are proposed to generate a better story in terms of coherence (Jain et al., 2017;, rationality , topic-consistence (Yao et al., 2018a).

most of story generation methods lack the ability to receive guidance from users to achieve a specific goal.


The model described above does not explicitly cater to the structure of the narration of recipes in the generation process.

we know that procedural text has a high level structure that carries a skeleton of the narrative.


Too low thresholds cause a large amount of constraints, which adversely affect paraphrase quality.

with a high threshold, the proposed method can achieve high performance stably.


Sequence-to-sequence (seq2seq) models have achieved tremendous success in text generation tasks.

there is no guarantee that they can always generate sentences without grammatical errors.


We also conduct experiments on the WMT17 Automatic Post-Editing (APE) task.

we observe a large number of grammatical errors in the references which make the automatic evaluation less reliable.


For most sentences edited by GEC, their grammaticality is improved; while the bad cases are only in a small proportion (≤10%) in all the six tasks.

the task-oriented improvements vary across the tasks.


The two questions in a question pair in the Quora dataset are typically very similar in meaning.

the WikiAnswers paraphrase corpus tends to be noisier but one source question is paired with multiple target questions.


However, both stages are largely isolated in the status quo and, hence, information from the two phases is never properly fused.

this work proposes RankQA 1 : RankQA extends the conventional two-stage process in neural QA with a third stage that performs an additional answer reranking.


Adaptive retrieval, like many other recent advancements (e. g., Lee et al., 2018;Lin et al., 2018), limits the amount of information that flows between the information retrieval and machine comprehension modules in order to select better answers.

rankQA does not limit the information, but directly re-ranks the answers to remove noisy candidates.


The Curat-edTREC or WebQuestions datasets, for instance, are highly sensitive to some information retrieval features.

in all cases, the fused combination of features from both information retrieval and machine comprehension is crucial for obtaining a strong performance.


Earlier systems of this type comprise various modules such as, for example, query reformulation (e. g., Brill et al., 2002), question classification (Li and Roth, 2006), passage retrieval (e. g., Harabagiu et al., 2000), or answer extraction (Shen and Klakow, 2006).

the aforementioned modules have been reduced to two consecutive steps with the advent of neural QA.


(2018) re-rank the paragraphs before forwarding them to machine comprehension.

none of the listed works introduce answer reranking to neural QA.


Inference & Learning Challenges The model described above is conceptually simple.

inference and learning are challenging since 1  open evidence corpus presents an enormous search space (over 13 million evidence blocks), and (2) how to navigate this space is entirely latent, so standard teacher-forcing approaches do not apply.


We find that ORQA is more robust at separating semantically distinct text with high lexical overlap, as shown in the first three examples.

it is expected that there are limits to how much information can be compressed into 128-dimensional vectors.


In multi-hop reading comprehension, a system answers a question over a collection of paragraphs by combining evidence from multiple paragraphs.

to single-hop reading comprehension, in which a system can obtain good performance using a single sentence (Min et al., 2018), multi-hop reading comprehension typically requires more complex reasoning over how two pieces of evidence relate to each other.


and "Which animal races annually for a national title as part of ANS?".

in the given set of paragraphs, there are multiple games that can be the answer to the first sub-question.


the SimpleQuestion dataset, 99% of the relations in the test set also exist in the training data, which means their embeddings could be learned well during training.

for those relations which are never seen in the training data (called unseen relations), their embeddings have never been trained since initialization.


Each sample in SQ includes a human annotated question and the corresponding knowledge triple.

the distribution of the relations in the test set is unbalanced.


In this paper, we use knowledge graph embedding as a bridge between seen and unseen relations, which shares the same spirit with previous work.

less study has been done in relation detection.


To address this, recent studies propose to build entity graphs from input paragraphs and apply graph neural networks (GNNs) to aggregate the information through entity graphs (Dhingra et al., 2018;De Cao et al., 2018;Song et al., 2018a).

all of the existing work apply GNNs based on a static global entity graph of each QA pair, which can be considered as performing implicit reasoning.


WikiHop) usually aggregates document information to an entity graph, and answers are then directly selected on entities of the entity graph.

in a more realistic setting, the answers may even not reside in entities of the extracted entity graph.


Many IR techniques can be applied to answer single-hop questions (Rajpurkar et al., 2016).

these IR techniques are hardly introduced in multi-hop QA, since a single fact can only partially match a question.


Using Tok2Ent and dynamic graph attention, we realize a reasoning step at the entity level.

the unrestricted answer still cannot be backtraced.


Rule-based models are attractive for various tasks because they inherently lead to interpretable and explainable decisions and can easily incorporate prior knowledge.

such systems are difficult to apply to problems involving natural language, due to its linguistic variability.


Moreover, such models tend to require large amounts of training data to generalise correctly, and incorporating background knowledge is still an open problem (Rocktäschel et al., 2015;Weissenborn et al., 2017a;Rocktäschel and Riedel, 2017;Evans and Grefenstette, 2017).

rule-based models are easily interpretable, naturally produce explanations for their decisions, and can generalise from smaller quantities of data.


In contrast, rule-based models are easily interpretable, naturally produce explanations for their decisions, and can generalise from smaller quantities of data.

these methods are not robust to noise and can hardly be applied to domains where data is ambiguous, such as vision and language (Moldovan et al., 2003;Rocktäschel and Riedel, 2017;Evans and Grefenstette, 2017).


Datalog (Gallaire and Minker, 1978) programs where all function symbols have arity zero and are called entities and, similarly to related work (Sessa, 2002;Julián-Iranzo et al., 2009), we disregard negation and disjunction.

in principle NLPROLOG also supports functions with higher arity.


The worst case complexity vanilla logic programming is exponential in the depth of the proof (Russell and Norvig, 2010a).

in our case, this is a particular problem because weak unification requires the prover to attempt unification between all entity and predicate symbols.


Compared to statistical machine learning-based methods (Kushman et al., 2014;Mitra and Baral, 2016;Roy and Roth, 2018;Zhou et al., 2015;Huang et al., 2016) and semantic parsing-based methods (Shi et al., 2015;Koncel-Kedziorski et al., 2015;Roy and Roth, 2015;Huang et al., 2017), these methods do not need hand-crafted features and achieve high performance on large datasets.

they lack in capturing the specific MWPs features, which are an evidently vital component in solving MWP.


The results in Table 2 are quite positive: workers almost always provide the correct answer a to our implication question q when given only the original (q, a) pair and no additional context, which indicates the implication is valid.

workers under-predict and are inaccurate for the control questions, which is expected since there is no necessary logical connection between (q, a) and the control question.


Such attention weights are not readily available for all NMT models.

our method can be used to fine-tune arbitrary NMT models to reduce word omission errors in only hundreds of steps.


Results A logistic regression model is fit using all the features, and the resulting coefficients, shown in Figure 1, provide support for all five hypotheses.

the effect sizes of each hypotheses variables differed substantially, pointing to the complexity of code switching behavior.


Notably, we find that topical modulation has the largest effect on switching to Naijá, with use of emotion surpassing the effect for a few topics.

as no one factor was sufficient for predicting code switching, our results point to the need for holistically modeling the social context when examining factors influence code-switching behavior.


Compared to degree centrality, PageRank can in theory be better since the global graph structure is considered.

we only observed marginal differences in our experiments (see Sections 4 and 5 for details).


We adopted the training, validation and test splits (589,284/32,736/32,739) widely used for evaluating abstractive summarization systems.


Training takes 1 minute for a model with cross-entropy loss and 30 minutes for a model with joint loss on an NVidia V100 GPU.

reducing the step size in IG for calculating Riemann approximation of the integral to 10 steps reduces the training time to 6 minutes.


Hence, interpretability methods tailored for text are quite sparse (Mudrakarta et al., 2018;Jia and Liang, 2017;Murdoch et al., 2018).

there are many papers criticizing the aforementioned methods by questioning their faithfulness, correctness (Adebayo et al., 2018;Kindermans et al., 2017) and usefulness.


From a fairness angle, our technique shares similarities with adversarial training (Zhang et al., 2018a;Madras et al., 2018) in asking the model to optimize for an additional objective that transitively unbiases the classifier.

those approaches work to remove protected attributes from the representation layer, which is unstable.


Similarly, comparing our method XIV with methods VII-IX, they all use the same term-based similarities.

our method achieves significantly better performance by using graphical decomposition.


This is reasonable, as using each keyword directly as a concept vertex provides more anchor points for article comparison .

community detection can group highly coherent keywords together and reduces the average size of CIGs from 30 to 13 vertices.


Traditional methods represent a text document as vectors of bag of words (BOW), term frequency inverse document frequency (TF-IDF), LDA (Blei et al., 2003) and so forth, and calculate the distance between vectors.

they cannot capture the semantic distance and usually cannot achieve good performance.


Finally, pre-training models such as BERT (Devlin et al., 2018) can also be utilized for text matching.

the model is of high complexity and is hard to satisfy the speed requirement in real-world applications.


By extending this model with a few additional features, such as the Kullback-Leibler divergence and related measures, we were able to reach the performance of the best verifiers submitted so far.

1 reality caught up with us when we applied our verifier to other authorship verification problems with little success.


Comparing with the distribution of market comments, few article titles use decimal.

writers of articles use more 4th-magnitude numerals than those in market comments.


Evaluation measures: Common LMTC evaluation measures are precision (P @K) and recall (R@K) at the top K predicted labels, averaged over test documents, micro-averaged F1 over all labels, and nDCG@K (Manning et al., 2009).

p @K and R@K unfairly penalize methods when the gold labels of a document are fewer or more than K, respectively.


HAN also performs better than CNN-LWAN for all and frequent labels.

replacing the CNN encoder of CNN-LWAN with a BIGRU (BIGRU-LWAN) leads to the best results, indicating that the main weakness of CNN-LWAN is its vanilla CNN encoder.


Systems also vary in how they incorporate feedback, such as "must- * Work performed at University of Maryland, College Park link" and "cannot-link" constraints (Andrzejewski et al., 2009;Hu et al., 2014), informed priors (Smith et al., 2018), or document labels .

evaluations of these systems are either not comparative (Choo et al., 2013;Lee et al., 2017) or compare against noninteractive models (Hoque and Carenini, 2015;Hu et al., 2014) or for only a limited set of refinements Xie et al., 2015).


For remove word, and merge topics, all methods provide good control (scores close to 1.0).

the informed prior methods, info-vb and info-gibbs, provide more control, for both the random (C Rand ) and good (C Good ) users, compared to const-gibbs.


const-gibbs supports defining token and document-level constraints, which ensure almost perfect control for refinements that require restricting certain words or documents, such as remove word and remove document.


SST in (Buch et al., 2017) effectively generates proposals in a single pass.

previous methods do not consider context information to produce nonoverlapped procedures.


On one hand, only the full model can generate eggs in the procedure (1.1) and (1.2), which is also an important ingredient entity in the ground-truth captions.

the ingredient bacon in groundtruth caption (c) is ignored by all models.


Similar to our work, some of these previous datasets have considered everyday routine actions (Fabian Caba Heilbron and Niebles, 2015;Real et al., 2017;Kay et al., 2017).

because these datasets rely on videos uploaded on YouTube, it has been observed they can be potentially biased towards unusual situations (Kay et al., 2017).


This type of linguistic reasoning is critical for interactions grounded in visually complex environments, such as in robotic applications.

commonly used resources for language and vision (e.g., Antol et al., 2015;Chen et al., 2016) focus mostly on identification of object properties and few spatial relations (Section 4; Ferraro et al., 2015;Alikhani and Stone, 2019).


In a final experiment, we learn to model language that describes images, and we find that conditioning on visual context improves segmentation performance in our model (compared to the performance when the model does not have access to the image).

in a baseline model that predicts boundaries based on entropy spikes in a character-LSTM, making the image available to the model has no impact on the quality of the induced segments, demonstrating again the value of explicitly including a word lexicon in the language model.


The choice makes sense, as reinforcement learning is well suited for tasks that require a set of actions to reach a goal.

the performance of these models have been sub-optimal when compared to the average human performance on the same task.


Comparing row 1 versus rows 2 and 3 we see that adding the SPEECH/TEXT task leads to a substantial improvement.

comparing rows 2 and 3 versus rows 6 and 11, it seems that the addition of TEXT/IMAGE task does not seem to have a major impact on performance, at least to the extent that can be gleaned from the experiments we carried out.


Recent advances in sequence modeling have highlighted the strengths of the transformer architecture, especially in achieving state-of-theart machine translation results.

depending on the upstream systems, e.g., speech recognition, or word segmentation, the input to translation system can vary greatly.


Although in principle, it is not necessary to mask for self-attentions in the encoder, in practical implementation it is required to mask the padding positions.

self-attention in the decoder only allows positions up to the current one to be attended to, preventing information flow from the left and preserving the auto-regressive property.


4): Finally, as in the vanilla Transformer decoder, the third sub-layer is a feed-forward network that processes the representation for the next n+1 layer: The three sources of information (image, object labels, and web entity labels) are treated symmetrically in the above equations.

the only "true" source of information in this case is the image, whereas the other labels are automaticallyproduced annotations that can vary both in quality and other properties (e.g., redundancy).


Due to the limited number of clip arts, the vocabulary is smaller than it would be for real images.

humans still use compositional language to describe clip art configurations and attributes, and make references to previous discourse elements in their messages.


After each nextutterance token, our neural Drawer model is used to take an action in the scene and the resulting change in scene similarity metric is used as a reward.

this reward scheme alone has an issue: once all objects in the scene are described, any further messages will not result in a change in the scene and have a reward of zero.